{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.2 - Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2.1. Hierarchical Clustering Example using Dummy Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.1.1. Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# generating dummy data of 10 records with 2 clusters\n",
    "features, labels = make_blobs(n_samples=10, centers=2, cluster_std = 2.00)\n",
    "\n",
    "#plotting the dummy data\n",
    "plt.scatter(features[:,0], features[:,1], color ='r' )\n",
    "\n",
    "#adding numbers to data points\n",
    "annots = range(1, 11)\n",
    "for label, x, y  in zip(annots, features[:, 0], features[:, 1]):\n",
    "    plt.annotate(\n",
    "        label,\n",
    "        xy=(x, y), xytext=(-3, 3),\n",
    "        textcoords='offset points', ha='right', va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -6.05229603,   6.34531886],\n",
       "       [ -8.18818517,   5.20965226],\n",
       "       [-11.92364385,   4.83780819],\n",
       "       [  2.01401686,  -0.98836642],\n",
       "       [ -5.77977017,   5.10974293],\n",
       "       [  7.01195527,   1.15811345],\n",
       "       [  6.99651232,  -2.14471545],\n",
       "       [  5.50952101,  -5.26821336],\n",
       "       [  2.59754305,  -2.33863444],\n",
       "       [ -5.32881469,   3.02595732]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD7CAYAAABDld6xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQWUlEQVR4nO3de5BkZX3G8e/jgkFAgisjKqgrieyqoGCmNGp5iaDcjCQRKhAvCRWzKQ1GU5rEVCURjVVWKimN8Ra3xJWINyAgia6oFcQES9EBkRXYtRS8cNkwuNzlovjLH90D6zDs9LJ9Tr/LfD9VXbM9fabPw9Dz9DvvvOecVBWSpHY9ZNIBJElbZ1FLUuMsaklqnEUtSY2zqCWpcTt18aR77bVXrVixoounlqQHpQsvvPD6qppa6LFOinrFihXMzMx08dSS9KCU5If395hTH5LUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGdXLAy47sExf8iLMvvnrSMaTeHX3QPvzBsx4/6RhagCPqec6++Gouu/bmSceQenXZtTc7QGmYI+oFPOUxe/DpP332pGNIvfn9D31t0hG0FY6oJalxFrUkNc6ilqTGWdSS1LiRijrJXyS5NMl3knwyyS5dB5MkDSxa1En2Af4cmK6qA4BlwHFdB5MkDYw69bET8LAkOwG7Atd0F0mStKVFi7qqrgb+GfgRcC1wU1V9cf52SVYnmUkyMzs7O/6kkrREjTL18QjgaOCJwGOB3ZK8cv52VbWmqqaranpqasHrM0qSHoBRpj4OBa6sqtmq+hlwJvCcbmNJkuaMUtQ/An4zya5JAhwCXN5tLEnSnFHmqC8AzgAuAtYPv2ZNx7kkSUMjnZSpqt4KvLXjLJKkBXhkoiQ1zqKWpMZZ1JLUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGWdSS1DiLWpIaZ1FLUuMsaklqnEUtSY0b5ZqJK5NcvMXt5iRv7COcJGmECwdU1UbgIIAky4CrgbM6ziVJGtrWqY9DgO9X1Q+7CCNJuq9tLerjgE92EUSStLCRizrJQ4GXAaffz+Ork8wkmZmdnR1XPkla8rZlRH0EcFFV/d9CD1bVmqqarqrpqamp8aSTJG1TUR+P0x6S1LuRijrJrsCLgTO7jSNJmm/R5XkAVfVT4JEdZ5EkLcAjEyWpcRa1JDXOopakxlnUktQ4i1qSGmdRS1LjLGpJapxFLUmNs6glqXEWtSQ1zqKWpMZZ1JLUOItakhpnUUtS4yxqSWqcRS1JjRv1Ci97JjkjyYYklyd5dtfBJEkDI13hBXgPcE5VHTO8GvmuHWaSJG1h0aJOsgfwfOCPAKrqLuCubmNJkuaMMvWxHzALrE3yrSQfTrLb/I2SrE4yk2RmdnZ27EElaakapah3Ap4BfLCqDgZuA94yf6OqWlNV01U1PTU1NeaYkrR0jVLUVwFXVdUFw/tnMChuSVIPFi3qqtoE/DjJyuGnDgEu6zSVJOkeo676eD3w8eGKjyuAE7qLJEna0khFXVUXA9MdZ5EkLcAjEyWpcRa1JDXOopakxlnUktQ4i1qSGmdRS1LjLGpJapxFLUmNs6glqXEWtSQ1zqKWpMZZ1JLUOItakhpnUUtS4yxqSWqcRS1JjRvpwgFJfgDcAtwN/LyqvIiAJPVk1EtxAfxWVV3fWRJJ0oKc+pCkxo1a1AV8McmFSVYvtEGS1UlmkszMzs6OL6EkLXGjFvVzq+oZwBHAnyV5/vwNqmpNVU1X1fTU1NRYQ0rSUjZSUVfVNcOP1wFnAc/sMpQk6V6LFnWS3ZI8fO7fwEuA73QdTJI0MMqqj72Bs5LMbf+Jqjqn01SSpHssWtRVdQXw9B6ySJIW4PI8SWqcRS1JjbOoJalxFrUkNc6ilqTGWdSS1DiLWpIaZ1FLUuMsaklqnEUtSY2zqCWpcRa1JDXOopakxlnUktQ4i1qSGjfKhQMkjcnp3z2ddVesm3SM+9i4+QUAnHDOmgknua8j9zuSY/c/dtIxJmrkok6yDJgBrq6ql3YXSXrwWnfFOjZu3sjK5SsnHeWXHHzwVyYdYUEbN28EsKi3Yds3AJcDe3SURVoSVi5fydrD1046xg7hhHNOmHSEJow0R51kX+Ao4MPdxpEkzTfqHxP/Bfgr4Bf3t0GS1UlmkszMzs6OJZwkaYSiTvJS4LqqunBr21XVmqqarqrpqampsQWUpKVulBH1c4GXJfkB8CngRUlO7TSVJOkeixZ1Vf1NVe1bVSuA44Bzq+qVnSeTJAEe8CJJzdumA16q6jzgvE6SzJlZC+vP6HQXW7Xp6MHHte+YzP4PPAamXZIk6V7tHZm4/gzYtB4efeBEdv/px589kf0Cg/9usKgl/ZL2ihoGJX3C5yadon9rj5p0AkkNco5akhpnUUtS4yxqSWqcRS1JjWvzj4mSmtfHubU3bN4AdH8WvdbPee2IWtIDMndu7S6tWr6KVctXdbqPjZs3Nnkxhy05opb0gD0Yzq29I5zz2hG1JDXOEbWkHdY45snHNQ/e5Ty3I2pJO6xxzJOPYx6863luR9SSdmgtzJN3Pc/tiFqSGmdRS1LjRrlm4i5JvpHk20kuTfK2PoJJkgZGmaO+E3hRVd2aZGfg/CSfr6qvd5xNksQIRV1VBdw6vLvz8FZdhpIk3WukOeoky5JcDFwHfKmqLug2liRpzkhFXVV3V9VBwL7AM5McMH+bJKuTzCSZmZ2dHXdOSVqytmnVR1XdyODitocv8NiaqpququmpqakxxZMkjbLqYyrJnsN/Pww4FNjQdTBJ0sAoqz4eA5ySZBmDYj+tqj7bbSxJ0pxRVn1cAhzcQxZJ0gI8MlGSGmdRS1LjLGpJapxFLUmNs6glqXEWtSQ1zqKWpMZZ1JLUOK+ZeH9m1sL6M/rd56ZLBh/XHtXvfg88Bqa7veabpAfOEfX9WX8GbFrf7z4f/bTBrU+b1vf/hiRpmzii3ppHHwgnfG7SKbrV9+hd0jZzRC1JjbOoJalxFrUkNc6ilqTG+cfElvWxRLCvJYEuAZQesFEuxfW4JF9OcnmSS5O8oY9gop8lgn0sCXQJoLRdRhlR/xx4U1VdlOThwIVJvlRVl3WcTfDgWCLoEkBpuyw6oq6qa6vqouG/bwEuB/bpOpgkaWCb/piYZAWD6ydesMBjq5PMJJmZnZ0dTzpJ0uhFnWR34D+AN1bVzfMfr6o1VTVdVdNTU1PjzChJS9pIRZ1kZwYl/fGqOrPbSJKkLY2y6iPAycDlVfWu7iNJkrY0yoj6ucCrgBcluXh4O7LjXJKkoUWX51XV+UB6yCJJWoCHkEtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGeT5qaRuc/t3TWXfFugf89Rs2bwDghHO279zcR+53JMfuf+x2PYd2HI6opW2w7op1bNy88QF//arlq1i1fNV2Zdi4eeN2vVlox+OIWtpGK5evZO3haye2/+0djWvH44hakhpnUUtS45z60OK29yK747qArhfI1RLliFqL296L7I7jArpeIFdLmCNqjWbSF9n1ArlawhxRS1LjLGpJatwol+L6SJLrknynj0CSpF82yoj6o8DhHeeQJN2PRYu6qv4H2NxDFknSAsY2R51kdZKZJDOzs7PjelpJWvLGVtRVtaaqpqtqempqalxPK0lLnqs+JKlxFrUkNW6U5XmfBL4GrExyVZI/7j6WJGnOooeQV9XxfQSRJC3MqQ9JapxFLUmNs6glqXEWtSQ1zqKWpMZZ1JLUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGWdSS1DiLWpIaZ1FLUuNGKuokhyfZmOR7Sd7SdShJ0r1GuRTXMuD9wBHAU4Djkzyl62CSpIFRRtTPBL5XVVdU1V3Ap4Cju40lSZqTqtr6BskxwOFV9Zrh/VcBz6qqE+dttxpYPby7Etg4/riS9KD1hKqaWuiBRS9uC2SBz92n3atqDbBmG4NJkhYxytTHVcDjtri/L3BNN3EkSfONUtTfBJ6U5IlJHgocB/xnt7EkSXMWnfqoqp8nORH4ArAM+EhVXdp5MkkSMMIfEyVJk+WRiZLUOItakhpnUUtS45oq6iSnJrk2yc1JvpvkNRPI8CtJTk7ywyS3JPlWkiN62O+JSWaS3Jnko/MeOyTJhiQ/TfLlJE/oOs9imfqU5LwkdyS5dXib2MFUSZ40zHLqhPb/5CTnJrlpeO6d351QjhVJ1iW5IcmmJO9LMspxGePOcVySy5PcluT7SZ7X8/5vnXe7O8l7x72fpooaeCewoqr2AF4GvCPJb/ScYSfgx8ALgF8F/g44LcmKjvd7DfAO4CNbfjLJXsCZwxzLgRng0x1n2WqmCTmxqnYf3lZOMMf7GSxZ7d2wCM8GPsvgtbAaODXJ/hOI8wHgOuAxwEEMfl5e12eAJC8G/hE4AXg48Hzgij4zbPGa3B3YG7gdOH3c+2mqqKvq0qq6c+7u8PZrPWe4rapOqqofVNUvquqzwJVAp28YVXVmVX0G+Mm8h34PuLSqTq+qO4CTgKcnWdVlnkUyLUlJjgNuBP57QhFWAY8F3l1Vd1fVucBXgVdNIMsTgdOq6o6q2gScAzy15wxvA95eVV8f/qxeXVVX95xhS8cwePP633E/cVNFDZDkA0l+CmwArgXWTTjP3sD+wKTWjj8V+Pbcnaq6Dfg+/f9QTNo7k1yf5KtJXtj3zpPsAbwdeFPf+94yxv187oC+gwDvAY5LsmuSfRicXfOcvnY+PKvnNDA1nAK6ajj98rC+MizgD4F/rw7WPDdX1FX1Oga/xjyPwa/8d279K7qTZGfg48ApVbVhQjF2B26a97mbGHyPloq/BvYD9mFwPpn/StLrb1rAPwAnV9WPe97vljYwGLH9ZZKdk7yEwZTDrhPI8hUGg4WbGZxmYgb4TI/73xvYmcEo9nkMpl8OBv62xwz3SPJ4Bv8vTuni+ZsraoDhr3XnMzivyGsnkSHJQ4CPAXcBJy6yeZduBfaY97k9gFsmkGUiquqCqrqlqu6sqlMY/Lp/ZF/7T3IQcCjw7r72uZCq+hnwO8BRwCYGo/vTGBRlb4Y/G19gMJDaDdgLeASD+eK+3D78+N6quraqrgfeRY+vi3leDZxfVVd28eRNFvUWdqLnOWqAJAFOZvCu/fLhD8ikXAo8fe5Okt0YfE+W8mH8xcLTAF15IbAC+FGSTcCbgZcnuajHDABU1SVV9YKqemRVHcbgN41v9BxjOYMTtb1v+Ob5E2AtPZZkVd3A4A2qlUOrX01Ho2loqKiTPGq41Gb3JMuSHAYcD5w7gTgfBJ4M/HZV3b7YxuOQZKckuzA4n8qyJLsM/8p/FnBAkpcPH/974JI+pmK2kqk3SfZMctjcvpO8gsFf97/QY4w1DN4cDxre/g34HHBYjxkASPK04fdi1yRvZrDq4qN9ZhiOXq8EXjv8f7Ing/nZb2/9K8duLfD6YXc8AngjgxUxvUryHAbTcmNf7XGPqmriBkwxmPe6kcG813rgTyaQ4wkM3qXvYDDtMHd7Rcf7PYl7V7rM3U4aPnYog/nJ24HzGCxh7ON7cb+Zen5dfJPBVM+NwNeBF/f9uljg+3LqhPb9T8ANw9fk54Ffn1COg4avxRuA6xmU1KN6zrAzg2WCNzKYCvpXYJcJfC8+BHysy314UiZJalwzUx+SpIVZ1JLUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNe7/ATHJtdXUXvotAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "\n",
    "\n",
    "dendos = linkage(features, 'single')\n",
    "\n",
    "annots = range(1, 11)\n",
    "\n",
    "dendrogram(dendos,\n",
    "            orientation='top',\n",
    "            labels=annots,\n",
    "            distance_sort='descending',\n",
    "            show_leaf_counts=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 1, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "# training agglomerative clustering model\n",
    "hc_model = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='ward')\n",
    "hc_model.fit_predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d6d78ead90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pring the data points\n",
    "plt.scatter(features[:,0], features[:,1], c= hc_model.labels_, cmap='rainbow' )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.1.2. Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d6d794d730>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generating dummy data of 500 records with 4 clusters\n",
    "features, labels = make_blobs(n_samples=500, centers=4, cluster_std = 2.00)\n",
    "\n",
    "#plotting the dummy data\n",
    "plt.scatter(features[:,0], features[:,1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 0, 3, 0, 0, 1, 0, 0, 1, 3, 0, 2, 0, 3, 1, 0, 0, 0,\n",
       "       2, 1, 2, 1, 2, 1, 0, 2, 2, 3, 1, 2, 1, 0, 1, 1, 0, 0, 1, 1, 3, 3,\n",
       "       1, 1, 2, 1, 1, 2, 0, 3, 3, 0, 1, 0, 1, 0, 2, 2, 0, 0, 3, 1, 0, 3,\n",
       "       3, 0, 1, 3, 3, 1, 1, 1, 3, 0, 0, 3, 3, 2, 0, 0, 0, 3, 0, 3, 0, 1,\n",
       "       3, 2, 1, 2, 1, 3, 0, 1, 2, 2, 3, 2, 3, 0, 2, 3, 0, 2, 1, 2, 3, 2,\n",
       "       3, 2, 2, 2, 1, 1, 0, 1, 0, 3, 1, 1, 0, 1, 1, 0, 3, 3, 0, 3, 0, 2,\n",
       "       1, 3, 2, 2, 1, 3, 2, 2, 1, 2, 3, 2, 0, 3, 2, 1, 0, 2, 2, 0, 0, 0,\n",
       "       0, 3, 2, 3, 3, 1, 1, 1, 2, 0, 2, 2, 0, 2, 0, 3, 2, 3, 2, 2, 3, 1,\n",
       "       1, 1, 2, 3, 0, 2, 1, 0, 3, 2, 2, 3, 2, 0, 0, 1, 2, 2, 2, 0, 2, 3,\n",
       "       2, 3, 2, 2, 3, 2, 2, 1, 0, 0, 1, 0, 3, 3, 3, 1, 2, 2, 1, 3, 1, 0,\n",
       "       1, 1, 1, 3, 3, 3, 2, 3, 2, 0, 3, 1, 2, 3, 1, 3, 1, 1, 2, 0, 0, 3,\n",
       "       1, 3, 3, 3, 2, 0, 0, 2, 1, 3, 2, 0, 3, 2, 3, 0, 3, 3, 3, 3, 3, 0,\n",
       "       3, 1, 3, 3, 1, 2, 0, 3, 1, 0, 0, 1, 1, 0, 2, 1, 1, 3, 2, 2, 0, 1,\n",
       "       0, 2, 2, 0, 1, 1, 0, 2, 0, 1, 3, 2, 2, 1, 1, 1, 2, 3, 0, 2, 0, 0,\n",
       "       1, 2, 2, 1, 2, 0, 0, 1, 0, 1, 2, 0, 2, 3, 3, 3, 1, 0, 3, 0, 1, 2,\n",
       "       0, 0, 3, 1, 1, 2, 3, 3, 2, 1, 0, 1, 2, 1, 2, 3, 2, 0, 3, 0, 2, 3,\n",
       "       0, 2, 2, 2, 3, 3, 3, 0, 1, 0, 2, 1, 2, 3, 1, 2, 0, 3, 2, 2, 0, 0,\n",
       "       1, 2, 3, 3, 2, 1, 2, 0, 1, 0, 2, 1, 2, 1, 3, 0, 2, 2, 3, 3, 3, 2,\n",
       "       0, 3, 0, 2, 3, 2, 0, 3, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 0, 2,\n",
       "       2, 0, 0, 2, 2, 2, 2, 0, 2, 2, 3, 0, 0, 3, 3, 3, 3, 1, 3, 2, 0, 1,\n",
       "       0, 1, 3, 0, 3, 3, 1, 3, 0, 1, 0, 0, 2, 1, 3, 0, 3, 0, 3, 1, 3, 0,\n",
       "       1, 3, 3, 2, 0, 2, 0, 0, 0, 1, 1, 0, 2, 1, 0, 3, 2, 3, 0, 2, 3, 3,\n",
       "       3, 0, 1, 3, 0, 3, 2, 0, 2, 2, 3, 3, 1, 3, 3, 1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# performing kmeans clustering using AgglomerativeClustering class\n",
    "hc_model = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='ward')\n",
    "hc_model.fit_predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d6d79a1dc0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pring the data points\n",
    "plt.scatter(features[:,0], features[:,1], c= hc_model.labels_, cmap='rainbow' )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d6d79f99d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print actual datapoints\n",
    "plt.scatter(features[:,0], features[:,1], c= labels, cmap='rainbow' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2.2. Clustering of Iris Plants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "iris_df = sns.load_dataset(\"iris\")\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dividing data into features and labels\n",
    "features = iris_df.drop([\"species\"], axis = 1)\n",
    "labels = iris_df.filter([\"species\"], axis = 1)\n",
    "features.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
       "       2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training Hierarchical clustering model\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "# training agglomerative clustering model\n",
    "features = features.values\n",
    "hc_model = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')\n",
    "hc_model.fit_predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d6d7cc01f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pring the data points\n",
    "plt.scatter(features[:,0], features[:,1], c= hc_model.labels_, cmap='rainbow' )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as shc\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.title(\"Iris Dendograms\")\n",
    "dend = shc.dendrogram(shc.linkage(features, method='ward'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d6d7f606a0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# converting categorical labels to numbers\n",
    "\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "labels = le.fit_transform(labels)\n",
    "\n",
    "#pring the data points with original labels\n",
    "plt.scatter(features[:,0], features[:,1], c= labels, cmap='rainbow' )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 8.1\n",
    "\n",
    "\n",
    "### Question 1\n",
    "\n",
    "Which of the following is a supervised machine learning algorithm:\n",
    "\n",
    "A- K Means Clustering \\\n",
    "B- Hierarchical Clustering \\\n",
    "C- All of the above \\\n",
    "D- None of the above\n",
    "\n",
    "Answer: D\n",
    "    \n",
    "    \n",
    "### Question 2\n",
    "\n",
    "In KMeans clustering, the inertia tells us?\n",
    "\n",
    "A- the distance between datapoints withina  cluster\\\n",
    "B- output labels for the datapoints \\\n",
    "C- the number of clusters \\\n",
    "D- None of the above\n",
    "\n",
    "Answer: C\n",
    "\n",
    "\n",
    "### Question 3\n",
    "\n",
    "In hierarchical clustering, in case of vertical dendrograms, the number of clusters is equal the number of \\____ lines that the  \\____ line passes through?\n",
    "\n",
    "A- horizontal, vertical \\\n",
    "B- vertical, horizontal \\\n",
    "C- none of the above \\\n",
    "D- All of the above\n",
    "\n",
    "Answer: B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 8.2\n",
    "\n",
    "Apply KMeans clustering on the `banknote.csv` dataset available in the \"Resources\\Datasets\" folder. Find he optimal number of clusters and the print the clustered dataset. \n",
    "The following script imports the dataset and prints the first five rows of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d6d97341c0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "banknote_df = pd.read_csv(r\"E:\\Hands on Python for Data Science and Machine Learning\\Datasets\\banknote.csv\")\n",
    "banknote_df.head()\n",
    "\n",
    "### Solution:\n",
    "\n",
    "# dividing data into features and labels\n",
    "features = banknote_df.drop([\"class\"], axis = 1)\n",
    "labels = banknote_df.filter([\"class\"], axis = 1)\n",
    "features.head()\n",
    "\n",
    "# training KMeans on K values from 1 to 10\n",
    "loss =[]\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(n_clusters = i).fit(features)\n",
    "    loss.append(km.inertia_)\n",
    "\n",
    "#printing loss against number of clusters\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(range(1, 11), loss)\n",
    "plt.title('Finding Optimal Clusters via Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('loss')\n",
    "plt.show()\n",
    "\n",
    "# training KMeans with 3 clusters\n",
    "features = features.values\n",
    "km_model = KMeans(n_clusters=2)\n",
    "km_model.fit(features)\n",
    "\n",
    "#pring the data points with prediced labels\n",
    "plt.scatter(features[:,0], features[:,1], c= km_model.labels_, cmap='rainbow' )\n",
    "\n",
    "#print the predicted centroids\n",
    "plt.scatter(km_model.cluster_centers_[:, 0], km_model.cluster_centers_[:, 1], s=100, c='black')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
